Deep Transfer Learning in Mechanical Intelligent Fault Diagnosis: Application and Challenge-Reference-Cited by-同舟云学术

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Deep Transfer Learning in Mechanical Intelligent Fault Diagnosis: Application and Challenge

Published:2022-01-06 Issue:3 Volume:54 Page:2509-2531
ISSN:1370-4621
Container-title:Neural Processing Letters
language:en
Short-container-title:Neural Process Lett

Author:

Qian Chenhui,Zhu Junjun,Shen Yehu,Jiang Quansheng^ORCID,Zhang Qingkui

Funder

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,General Neuroscience,Software

Link

https://link.springer.com/content/pdf/10.1007/s11063-021-10719-z.pdf

Reference107 articles.

1. Lei Y, Jia F, Kong D, Lin J, Xing S (2018) Opportunities and challenges of machinery intelligent fault diagnosis in big data era. J Mech Eng 54(05):94–104

2. Hoang D, Kang H (2019) A survey on deep learning based bearing fault diagnosis. Neurocomputing 335:327–335

3. Wang Y, Wang Z (2021) Data-driven model-free adaptive fault-tolerant control for a class of discrete-time systems. IEEE Trans Circuits Syst II: Express Briefs 1–1

4. Zhang QH, Qin A, Lei S (2015) Vibration sensor based intelligent fault diagnosis system for large machine unit in petrochemical industries. Int J Distrib Sens Netw 13:239405

5. Sun G, Zhang Q, Shao L (2013) The build of a new non-dimensional indicator for fault diagnosis in rotating machinery. Int J Wirel Mobile Comput 6(3):271–276

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